mlr3summary: Concise and interpretable summaries for machine learning models
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866914771266174976 |
|---|---|
| author | Dandl, Susanne Becker, Marc Bischl, Bernd Casalicchio, Giuseppe Bothmann, Ludwig |
| author_facet | Dandl, Susanne Becker, Marc Bischl, Bernd Casalicchio, Giuseppe Bothmann, Ludwig |
| contents | This work introduces a novel R package for concise, informative summaries of machine learning models.
We take inspiration from the summary function for (generalized) linear models in R, but extend it in several directions:
First, our summary function is model-agnostic and provides a unified summary output also for non-parametric machine learning models;
Second, the summary output is more extensive and customizable -- it comprises information on the dataset, model performance, model complexity, model's estimated feature importances, feature effects, and fairness metrics;
Third, models are evaluated based on resampling strategies for unbiased estimates of model performances, feature importances, etc.
Overall, the clear, structured output should help to enhance and expedite the model selection process, making it a helpful tool for practitioners and researchers alike. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_16899 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | mlr3summary: Concise and interpretable summaries for machine learning models Dandl, Susanne Becker, Marc Bischl, Bernd Casalicchio, Giuseppe Bothmann, Ludwig Machine Learning This work introduces a novel R package for concise, informative summaries of machine learning models. We take inspiration from the summary function for (generalized) linear models in R, but extend it in several directions: First, our summary function is model-agnostic and provides a unified summary output also for non-parametric machine learning models; Second, the summary output is more extensive and customizable -- it comprises information on the dataset, model performance, model complexity, model's estimated feature importances, feature effects, and fairness metrics; Third, models are evaluated based on resampling strategies for unbiased estimates of model performances, feature importances, etc. Overall, the clear, structured output should help to enhance and expedite the model selection process, making it a helpful tool for practitioners and researchers alike. |
| title | mlr3summary: Concise and interpretable summaries for machine learning models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2404.16899 |